Detection of defects or anomalous structures is a common problem in applications where digital imaging techniques are used to monitor a process. This is especially common in manufacturing operations where digital cameras are commonly used as “computer vision systems” to acquire images of products as they are being manufactured. Images obtained can be used to identify and remove (or rework) defective manufactured products that can otherwise lead to product malfunction.
The most common techniques used to detect defects in digital images are reference-based. Referenced-based techniques rely on a reference image of a defect-free object to be used as a means of comparison. In the referenced-based technique, an image of a defect-free object is acquired, stored, and then used as a reference image. The reference image is sometimes referred to as a “golden image.” Image processing techniques are used to detect the differences between the golden image and an image of a new object. The detected differences between the images are then labeled as defects.
There are many applications in which a reference image is either unavailable or of insufficient quality to be used as a reliable basis for comparison. Detecting defects without the aid of a reference image is a difficult task for an automated computer vision system. If a reference (defect-free) image is unavailable, or is of insufficient quality to allow it to be used for defect detection, then non-reference based defect detection approaches must generally be used. However, to-date there are no available non-reference based methods for defect detection that consistently produce acceptable results. Moreover, available automated detection techniques are highly application specific in that the image processing techniques used are tuned for the specific object and background that are expected to be present in the image. As a result, such systems typically have limited application.